Mathematica Programming and Machine Learning

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Joshua Paul Steimel

3:07:30

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1 - Introduction

1 - Introduction to Mathematica and Basics of Programming.mp4

11:18

1 - MECH193A-Lecture-1-List-Creation-and-Extracting-Elements-of-Lists-in-Mathematica.mp4

11:55

1 - MECH-193A-Lecture-1-Creating-Functions-and-Conditional-Functions-in-Mathematica.mp4

05:30

1 - MECH-193A-Lecture-1-Helpful-Mathematica-Tips-and-Debugging.mp4

03:56

1 - MECH-193A-Lecture-1-Rules-Replacements-and-Table-Function-the-Most-Important-Function.mp4

05:23

2 - Plotting in Mathematica

2 - Lecture 2 Plotting In Mathematica.mp4

08:02

2 - MECH193A-Lecture-2-Bar-Pie-and-Parametric-Plots-in-Mathematica.mp4

10:54

2 - MECH-193A-Lecture-2-Error-Bar-Plots-in-Mathematica.mp4

06:05

2 - MECH-193A-Lecture-2-Plot3D-Plotting-List-of-Data-and-Histograms-in-Mathematica.mp4

11:02

3 - Image Processing in Mathematica

3 - Image Processing in Mathematica.mp4

03:06

3 - MECH-193A-Lecture-4-Computer-Vision-and-Facial-Feature-Classification.mp4

16:01

3 - MECH-193A-Lecture-4-Edge-Detect-Morphological-Components-Remove-Background-in-Mathematica.mp4

07:22

3 - MECH-193A-Lecture-4-Image-Displacements.mp4

04:39

3 - MECH-193A-Lecture-4-Measuring-Components-of-Images-in-Mathematica.mp4

07:53

3 - MECH-193A-Lecture-4-Rotate-Images-Flip-Images-and-Crop-Images-in-Mathematica.mp4

03:52

4 - Computer Vision and Machine Learning in Mathematica

4 - Computer Vision and Machine Learning.mp4

16:01

4 - MECH193A-Lecture-4-Machine-Learning-and-Neural-Networks-in-Mathematica.mp4

07:04

5 - Data Analysis and ANOVA in Mathematica

5 - Data Analysis and ANOVA in Mathematica.mp4

07:50

5 - MECH193A-Lecture-3-95-Confidence-Interval-Value-Calculations.mp4

01:46

5 - MECH193A-Lecture-3-One-Way-ANOVA-Analysis-in-Mathematica.mp4

04:02

5 - MECH193A-Lecture-3-Three-Way-ANOVA-Analysis-In-Mathematica.mp4

01:43

5 - MECH193A-Lecture-3-Two-Way-ANOVA-Analysis-in-Mathematica.mp4

02:45

5 - MECH193A-Lecture-3-T-Test-Comparison-and-Pvalue-Calculation-in-Mathematica.mp4

02:47

5 - MECH-193A-Lecture-3-Cleaning-Up-Data-and-Extracting-Key-Elements-of-Raw-Data-in-Mathematica.mp4

08:20

5 - MECH-193A-Lecture-3-Exporting-Files-in-Mathematica.mp4

04:21

5 - MECH-193A-Lecture-3-Statistical-Analysis-Confidence-Intervals-and-Z-Tables.mp4

07:31

5 - MECH-193A-Lecture-3-Statistics-on-Small-Sample-Sizes-and-t-Tables.mp4

06:22

Description

Fundamentals of Mathematica Programming and Applications

What You'll Learn?

Introduction to Coding In Mathematica
Perform Big Data Analysis
Statistical Analysis of Data
Perform Machine Learning and Artificial Intelligence

Who is this for?

People that want to learn about Mathematica

What You Need to Know?

No programming experience needed

More details

Description
Welcome to our comprehensive course on the Applications of Programming in Mathematica! This engaging program provides a thorough exploration of the basics and fundamentals of programming in Mathematica, equipping participants with essential skills to harness the power of this versatile computational platform.
Throughout the course, participants will delve into the intricacies of Mathematica programming, gaining a solid foundation that extends beyond mere syntax. The curriculum seamlessly integrates practical applications, emphasizing hands-on experience in diverse domains. Attendees will explore image processing techniques, mastering the art of manipulating and enhancing visual data using Mathematica's robust programming capabilities.
The course extends its reach into cutting-edge fields such as machine learning, where participants will learn to implement algorithms, build models, and analyze data to make informed decisions. Additionally, the program addresses the burgeoning realm of big data analysis, empowering learners to efficiently handle and interpret vast datasets using Mathematica's sophisticated programming tools.
By the course's conclusion, participants will have not only cultivated a deep understanding of Mathematica programming but also acquired practical skills applicable to real-world scenarios in image processing, machine learning, and big data analysis. Join us on this exciting journey, where theory meets hands-on application, and unlock the full potential of programming in Mathematica.
Who this course is for:
People that want to learn about Mathematica

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My name is Joshua Steimel and I am an Assistant Professor at the University of the Pacific. I received my B.S. and PhD Degree from MIT in the Department of Materials Science and Engineering. I love to teach materials and would love any feedback our courses you would like to take.

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